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BUT System Description to VoxCeleb Speaker Recognition Challenge 2019

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arxiv 1910.12592 v1 pith:VH6QY2IS submitted 2019-10-16 eess.AS cs.CLcs.SD

BUT System Description to VoxCeleb Speaker Recognition Challenge 2019

Hossein Zeinali , Shuai Wang , Anna Silnova , Pavel Mat\v{e}jka , Old\v{r}ich Plchot This is my paper
classification eess.AS cs.CLcs.SD
keywords systemschallengefixednetworksopenconditionsfusionkaldi
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this report, we describe the submission of Brno University of Technology (BUT) team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2019. We also provide a brief analysis of different systems on VoxCeleb-1 test sets. Submitted systems for both Fixed and Open conditions are a fusion of 4 Convolutional Neural Network (CNN) topologies. The first and second networks have ResNet34 topology and use two-dimensional CNNs. The last two networks are one-dimensional CNN and are based on the x-vector extraction topology. Some of the networks are fine-tuned using additive margin angular softmax. Kaldi FBanks and Kaldi PLPs were used as features. The difference between Fixed and Open systems lies in the used training data and fusion strategy. The best systems for Fixed and Open conditions achieved 1.42% and 1.26% ERR on the challenge evaluation set respectively.

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Cited by 4 Pith papers

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  1. Positive-Incentive Noise Predictor for Adversarial Purification in Speaker Verification

    eess.AS 2026-07 unverdicted novelty 6.0

    PnP reformulates adversarial purification as learning positive-incentive noise to defend speaker verification against attacks with high efficiency and limited impact on genuine utterances.

  2. PhiNet: Speaker Verification with Phonetic Interpretability

    eess.AS 2026-04 unverdicted novelty 6.0

    PhiNet adds phonetic interpretability to speaker verification while matching the accuracy of standard black-box models on VoxCeleb, SITW, and LibriSpeech.

  3. On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation

    cs.SD 2026-06 unverdicted novelty 4.0

    Diagnostic analysis of quantization errors in speaker verification models reveals a 2-bit knee point and supports a calibrated cascade that maintains FP32-level scores at reduced compute cost.

  4. ISCSLP 2026 CoT-TTS Challenge: Chain-of-Thought Reasoning for Context-Aware Text-to-Speech

    cs.SD 2026-06 unverdicted novelty 2.0

    The paper announces the ISCSLP 2026 CoT-TTS Challenge with text- and audio-context tracks, large-scale bilingual datasets, and a Qwen3-based baseline requiring both reasoning output and speech generation.